Coefficient of Variation Calculator (CV)
Enter a dataset and the tool applies CV = σ / μ × 100%, reporting both the sample (n − 1) and population (n) variants together with the mean, standard deviation and a quick interpretation against the conventional 10% / 30% thresholds. By normalising standard deviation against the mean, CV produces a unit-free, magnitude-free measure of relative dispersion — handy for comparing instrument repeatability, return volatility across stocks, or the consistency of two production batches.
Sample CV (uses n − 1)
42.76%
Standard choice for statistical inference: σ̂ / x̄ × 100%.
Sample standard deviation σ̂
2.1381
Population CV (uses n)
40.00%
Use this when the dataset is the entire population (e.g. all production batches).
Population standard deviation σ
2.0000
—
Summary statistics
- Data points n
- —
- Sum Σ
- —
- Mean μ
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Formula
CV = σ / μ (ratio) CV% = σ / μ × 100% (percent form) Sample: σ̂ = √( Σ(xᵢ − x̄)² / (n − 1) ) Population: σ = √( Σ(xᵢ − x̄)² / n ) Also known as the relative standard deviation (RSD); the two terms are interchangeable.
- · CV is only meaningful for ratio-scale data — variables with a true zero where multiplicative comparisons make sense (mass, length, concentration, returns). It is not appropriate for interval-scale variables like Celsius temperature or IQ scores, whose zero is arbitrary.
- · When the mean approaches zero the CV blows up (the denominator shrinks faster than the numerator). The tool reports "Undefined" when μ = 0 instead of returning Infinity.
- · Common engineering / analytical chemistry thresholds: CV < 10% is regarded as good repeatability, 10–30% is acceptable in many assays, and ≥ 30% usually triggers a check for outliers or a re-measurement.
- · Two flavours: sample CV uses n − 1 (Bessel's correction) for inference from a sample; population CV uses n when the data is the full population. Both are used in the literature — pick based on context.
- · For symmetric distributions (e.g. normal) CV agrees with σ / |x̄|; for skewed data such as financial returns, also report the median and IQR for a fuller picture.
- · Caveat: CV is sensitive to outliers via σ. For heavy-tailed data consider robust alternatives such as the Median Absolute Deviation or interquartile-range based dispersion measures.
Frequently asked
How is the coefficient of variation different from the standard deviation?
Standard deviation σ is an absolute measure of dispersion in the original units of the data. The coefficient of variation CV = σ / μ is a relative measure, expressed as a pure ratio (or percentage), so you can compare it across units and magnitudes. Example: a batch of screws has diameter σ = 0.02 mm and a batch of I-beams has length σ = 0.02 m — the σ numbers look identical but cannot be compared directly. Their CVs, however (screws μ = 5 mm → 0.4%, I-beams μ = 6 m → 0.33%) tell you which process is more consistent.
Why is CV used to compare investment risk?
In portfolio theory, standard deviation captures absolute volatility, but expected returns differ a lot across instruments — a growth stock might have μ = 15% with σ = 25% while a value stock has μ = 6% with σ = 12%. Comparing σ directly makes the growth stock look "riskier". CV answers the question "how much volatility per unit of expected return?": growth CV = 1.67, value CV = 2.00 — by that yardstick the value stock has more volatility per unit of return. Professional risk-adjusted return measures like the Sharpe ratio (which also subtracts the risk-free rate) are more refined, but CV is the natural entry point.
My data has a mean of zero or a negative mean — can I still compute CV?
Mean = 0: CV is mathematically undefined (zero in the denominator). The tool reports "Undefined" rather than ∞. In practice if your data fluctuates around zero (e.g. daily stock returns) CV is the wrong tool — compare standard deviations directly, or use a different ratio whose denominator is meaningful. Negative mean: this tool divides by |μ| and reports CV as a positive percentage (the more common convention in the literature). But beware: if values cross zero and the mean is close to zero, CV becomes very unstable — switch to a robust dispersion measure such as the MAD, or work on a log-transformed scale.
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